Background modeling in Temporal action localization has received some attention.
Several previous works [1]}, [2]} generate proposals by sliding window and classify them into \(C+1\)  classes for \(C\)  action classes plus background class.
Also, several studies attempt to explicit background modeling for weakly-supervised temporal action localization.
Some works [3]}, [4]} try to classify background frames as a separate class.
[5]} formulates background frames as out-of-distribution samples.
Essentially, all the above works aim to perform classification for these proposals.
Unlike them, in our work, we propose a Background Constraint concept to predict an additional background score for proposal confidence evaluation. To supervise the background score, we use temporal Intersection-over-Anchor (tIoA) between the proposal and the background. Our work concentrates on utilizing the background prediction score to restrict the confidence of proposals.
